Publications

Giampiero Gallo, Ostap Okhrin, und Giuseppe Storti. Dynamic tail risk forecasting: what do realized skewness and kurtosis add?. 20.09.2024. [PUMA: FIS_scads topic_engineering yaff]

Christian Genest, Ostap Okhrin, und Taras Bodnar. Copula modeling from Abe Sklar to the present day. Journal of Multivariate Analysis, (201)Academic Press Inc., Mai 2024. [PUMA: FIS_scads topic_engineering yaff]

Niklas Paulig, und Ostap Okhrin. An open-source framework for data-driven trajectory extraction from AIS data—The α-method. Ocean engineering, (312)Elsevier Science B.V., 15.11.2024. [PUMA: FIS_scads topic_engineering yaff]

Gong Chen, Hartmut Fricke, Ostap Okhrin, und Judith Rosenow. Flight delay propagation inference in air transport networks using the multilayer perceptron. Journal of air transport management, (114)Elsevier, Oxford u.a., Januar 2024. [PUMA: FIS_scads topic_engineering yaff]

Niklas Paulig, und Ostap Okhrin. Robust path following on rivers using bootstrapped reinforcement learning. Ocean engineering, (298)Elsevier Science B.V., 15.04.2024. [PUMA: FIS_scads topic_engineering yaff]

Jing Zou, Martin Odening, und Ostap Okhrin. Plant growth stages and weather index insurance design. Annals of actuarial science, (17)3:438--458, Cambridge University Press, 03.11.2023. [PUMA: FIS_scads topic_engineering yaff]

Martin Waltz, und Ostap Okhrin. Addressing maximization bias in reinforcement learning with two-sample testing. Artificial intelligence, (336)Elsevier Science B.V., November 2024. [PUMA: FIS_scads topic_engineering yaff]

Jing Zou, Martin Odening, und Ostap Okhrin. Data-driven determination of plant growth stages for improved weather index insurance design. Agricultural Finance Review, Emerald Group Publishing, Bingley, 2024. [PUMA: FIS_scads topic_engineering xack yaff]

Fabian Hart, Ostap Okhrin, und Martin Treiber. Vessel-following model for inland waterways based on deep reinforcement learning. Ocean engineering, (281)Elsevier Science B.V., August 2023. [PUMA: FIS_scads imported topic_engineering yaff]

Martin Waltz, und Ostap Okhrin. Spatial–temporal recurrent reinforcement learning for autonomous ships. Neural Networks, (2023)165:634--653, Elsevier Science B.V., 15.06.2023. [PUMA: FIS_scads topic_engineering yaff]

Ostap Okhrin, und Alexander Ristig. Penalized estimation of hierarchical Archimedean copula. Journal of Multivariate Analysis, (201)Academic Press Inc., 2023. [PUMA: FIS_scads topic_engineering yaff]

Fabian Hart, Martin Waltz, und Ostap Okhrin. Two-step dynamic obstacle avoidance. Knowledge-based systems, (302)Elsevier Science B.V., 25.10.2024. [PUMA: FIS_scads topic_engineering yaff]

Martin Waltz, Niklas Paulig, und Ostap Okhrin. 2-Level Reinforcement Learning for Ships on Inland Waterways: Path Planning and Following. 25.07.2023. [PUMA: FIS_scads topic_engineering yaff]

Lennart Schäpermeier, und Pascal Kerschke. Reinvestigating the R2 Indicator: Achieving Pareto Compliance by Integration. In Michael Affenzeller, Stephan M. Winkler, Anna V. Kononova, Thomas Bäck, Heike Trautmann, Tea Tusar, und Penousal Machado (Hrsg.), Parallel Problem Solving from Nature – PPSN XVIII, 202--216, Springer, Berlin u. a., 07.09.2024. [PUMA: FIS_scads topic_engineering xack yaff]

Jonathan Heins, Lennart Schäpermeier, Pascal Kerschke, und Darrell Whitley. Dancing to the State of the Art?: How Candidate Lists Influence LKH for Solving the Traveling Salesperson Problem. In Michael Affenzeller, Stephan M. Winkler, Anna V. Kononova, Thomas Bäck, Heike Trautmann, Tea Tusar, und Penousal Machado (Hrsg.), Parallel Problem Solving from Nature – PPSN XVIII, 100--115, Springer, Berlin u. a., 07.09.2024. [PUMA: FIS_scads topic_engineering xack yaff]

Konstantin Dietrich, Diederick Vermetten, Carola Doerr, und Pascal Kerschke. Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization. Proceedings of the Genetic and Evolutionary Computation Conference, 1007 -- 1016, Association for Computing Machinery (ACM), United States of America, 14.07.2024. [PUMA: FIS_scads imported topic_engineering yaff]

Johannes Gerritzen, Andreas Hornig, Peter Winkler, und Maik Gude. A methodology for direct parameter identification for experimental results using machine learning — Real world application to the highly non-linear deformation behavior of FRP. Computational Materials Science, (244 (2024))Elsevier Science B.V., September 2024. [PUMA: Constitutive FIS_scads area_architectures topic_engineering yaff]

Johannes Gerritzen, Andreas Hornig, Peter Winkler, und Maik Gude. Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning. ECCM21 - Proceedings of the 21st European Conference on Composite Materials, (3):1252--1259, European Society for Composite Materials (ESCM), 02.07.2024. [PUMA: Convolutional Direct FIS_scads Fiber Machine Strain area_architectures dependency, identification, learning, networks, neural parameter plastics rate reinforced topic_engineering yaff] URL